The present invention generally relates to yield analysis, and more specifically, to adaptive accelerated yield analysis.
When analyzing failure issues with complex systems that often have a very small rate of failure, typical approaches include using the Monte Carlo methods. For example, determining a rate at which a circuit fails (i.e., the ratio of the number of failures to the number of samples) when the failure rate is very small (i.e., one in a billion) can be a challenge. The Monte Carlo methods are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The mechanism driving these algorithms utilizes randomness to solve problems that might be deterministic in principle. Monte Carlo methods require every source of statistical variation to be sampled randomly according to the corresponding probability density function of the source of variation.